




'''simple docstring'''







import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=True      ,UpperCAmelCase="pt"						):






   '''simple docstring'''







   A__       =     {'add_prefix_space': True} if isinstance(UpperCAmelCase      ,UpperCAmelCase						) and not line.startswith(' '						) else {}
   A__       =     padding_side
   return tokenizer(
       [line]      ,max_length=UpperCAmelCase      ,padding='max_length' if pad_to_max_length else None      ,truncation=UpperCAmelCase      ,return_tensors=UpperCAmelCase      ,add_special_tokens=UpperCAmelCase      ,**UpperCAmelCase      ,)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=None      ,):






   '''simple docstring'''







   A__       =     input_ids.ne(UpperCAmelCase						).any(dim=0						)
   if attention_mask is None:
      return input_ids[:, keep_column_mask]
   else:
      return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])

class 	_snake_case(			UpperCAmelCase      ):






 def __init__(self       :  List[str]    ,		a       :  Any    ,		a       :  Union[str, Any]    ,		a       :  int    ,		a       :  Optional[int]    ,		a       :  Optional[Any]="train"    ,		a       :  Optional[int]=None    ,		a       :  Tuple=None    ,		a       :  str=None    ,		a       :  Dict=""    ,		)       ->   Dict:






    """simple docstring"""


    super().__init__()
    A__       =     Path(a    ).joinpath(type_path + '.source'    )
    A__       =     Path(a    ).joinpath(type_path + '.target'    )
    A__       =     self.get_char_lens(self.src_file    )
    A__       =     max_source_length
    A__       =     max_target_length
    assert min(self.src_lens    ) > 0, f"""found empty line in {self.src_file}"""
    A__       =     tokenizer
    A__       =     prefix
    if n_obs is not None:
       A__       =     self.src_lens[:n_obs]
    A__       =     src_lang
    A__       =     tgt_lang






 def __len__(self       :  Optional[Any]    )       ->   List[str]:






    """simple docstring"""


    return len(self.src_lens    )






 def __getitem__(self       :  Any    ,		a       :  str    )       ->   Dict[str, torch.Tensor]:






    """simple docstring"""


    A__       =     index + 1  # linecache starts at 1
    A__       =     self.prefix + linecache.getline(str(self.src_file    )    ,		a    ).rstrip('\n'    )
    A__       =     linecache.getline(str(self.tgt_file    )    ,		a    ).rstrip('\n'    )
    assert source_line, f"""empty source line for index {index}"""
    assert tgt_line, f"""empty tgt line for index {index}"""

    # Need to add eos token manually for T5
    if isinstance(self.tokenizer    ,		a    ):
       source_line += self.tokenizer.eos_token
       tgt_line += self.tokenizer.eos_token

    # Pad source and target to the right
    A__       =     (
        self.tokenizer.question_encoder if isinstance(self.tokenizer    ,		a    ) else self.tokenizer
    )
    A__       =     self.tokenizer.generator if isinstance(self.tokenizer    ,		a    ) else self.tokenizer

    A__       =     encode_line(a    ,		a    ,		self.max_source_length    ,		'right'    )
    A__       =     encode_line(a    ,		a    ,		self.max_target_length    ,		'right'    )

    A__       =     source_inputs['input_ids'].squeeze()
    A__       =     target_inputs['input_ids'].squeeze()
    A__       =     source_inputs['attention_mask'].squeeze()
    return {
        "input_ids": source_ids,
        "attention_mask": src_mask,
        "decoder_input_ids": target_ids,
    }






 @staticmethod
 def 						_UpperCamelCase (a       :  Tuple    )       ->   Tuple:






    """simple docstring"""


    return [len(a    ) for x in Path(a    ).open().readlines()]






 def 						_UpperCamelCase (self       :  str    ,		a       :  int    )       ->   Dict[str, torch.Tensor]:






    """simple docstring"""


    A__       =     torch.stack([x['input_ids'] for x in batch]    )
    A__       =     torch.stack([x['attention_mask'] for x in batch]    )
    A__       =     torch.stack([x['decoder_input_ids'] for x in batch]    )
    A__       =     (
        self.tokenizer.generator.pad_token_id
        if isinstance(self.tokenizer    ,		a    )
        else self.tokenizer.pad_token_id
    )
    A__       =     (
        self.tokenizer.question_encoder.pad_token_id
        if isinstance(self.tokenizer    ,		a    )
        else self.tokenizer.pad_token_id
    )
    A__       =     trim_batch(a    ,		a    )
    A__  ,			A__       =     trim_batch(a    ,		a    ,		attention_mask=a    )
    A__       =     {
        'input_ids': source_ids,
        'attention_mask': source_mask,
        'decoder_input_ids': y,
    }
    return batch




lowerCAmelCase_     						=				getLogger(__name__)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   return list(itertools.chain.from_iterable(UpperCAmelCase						)						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     get_git_info()
   save_json(UpperCAmelCase      ,os.path.join(UpperCAmelCase      ,'git_log.json'						)						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=4      ,**UpperCAmelCase						):






   '''simple docstring'''







   with open(UpperCAmelCase      ,'w'						) as f:
      json.dump(UpperCAmelCase      ,UpperCAmelCase      ,indent=UpperCAmelCase      ,**UpperCAmelCase						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   with open(UpperCAmelCase						) as f:
      return json.load(UpperCAmelCase						)







def 						_A      ( ):






   '''simple docstring'''







   A__       =     git.Repo(search_parent_directories=UpperCAmelCase						)
   A__       =     {
       'repo_id': str(UpperCAmelCase						),
       'repo_sha': str(repo.head.object.hexsha						),
       'repo_branch': str(repo.active_branch						),
       'hostname': str(socket.gethostname()						),
   }
   return repo_infos







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   return list(map(UpperCAmelCase      ,UpperCAmelCase						)						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   with open(UpperCAmelCase      ,'wb'						) as f:
      return pickle.dump(UpperCAmelCase      ,UpperCAmelCase						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   def remove_articles(UpperCAmelCase						):
      return re.sub(r'\b(a|an|the)\b'      ,' '      ,UpperCAmelCase						)

   def white_space_fix(UpperCAmelCase						):
      return " ".join(text.split()						)

   def remove_punc(UpperCAmelCase						):
      A__       =     set(string.punctuation						)
      return "".join(ch for ch in text if ch not in exclude						)

   def lower(UpperCAmelCase						):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase						)						)						)						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     normalize_answer(UpperCAmelCase						).split()
   A__       =     normalize_answer(UpperCAmelCase						).split()
   A__       =     Counter(UpperCAmelCase						) & Counter(UpperCAmelCase						)
   A__       =     sum(common.values()						)
   if num_same == 0:
      return 0
   A__       =     1.0 * num_same / len(UpperCAmelCase						)
   A__       =     1.0 * num_same / len(UpperCAmelCase						)
   A__       =     (2 * precision * recall) / (precision + recall)
   return fa







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   return normalize_answer(UpperCAmelCase						) == normalize_answer(UpperCAmelCase						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   assert len(UpperCAmelCase						) == len(UpperCAmelCase						)
   A__       =     0
   for hypo, pred in zip(UpperCAmelCase      ,UpperCAmelCase						):
      em += exact_match_score(UpperCAmelCase      ,UpperCAmelCase						)
   if len(UpperCAmelCase						) > 0:
      em /= len(UpperCAmelCase						)
   return {"em": em}







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   return model_prefix.startswith('rag'						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     {p: p for p in extra_params}
   # T5 models don't have `dropout` param, they have `dropout_rate` instead
   A__       =     'dropout_rate'
   for p in extra_params:
      if getattr(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):
         if not hasattr(UpperCAmelCase      ,UpperCAmelCase						) and not hasattr(UpperCAmelCase      ,equivalent_param[p]						):
            logger.info('config doesn\'t have a `{}` attribute'.format(UpperCAmelCase						)						)
            delattr(UpperCAmelCase      ,UpperCAmelCase						)
            continue
         A__       =     p if hasattr(UpperCAmelCase      ,UpperCAmelCase						) else equivalent_param[p]
         setattr(UpperCAmelCase      ,UpperCAmelCase      ,getattr(UpperCAmelCase      ,UpperCAmelCase						)						)
         delattr(UpperCAmelCase      ,UpperCAmelCase						)
   return hparams, config








'''simple docstring'''







import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
lowerCAmelCase_     						=				'''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase_     						=				direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
lowerCAmelCase_     						=				re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
lowerCAmelCase_     						=				re.compile(R'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowerCAmelCase_     						=				re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowerCAmelCase_     						=				[
    ('''pretraining''', '''MODEL_FOR_PRETRAINING_MAPPING_NAMES''', '''AutoModelForPreTraining'''),
    ('''feature-extraction''', '''MODEL_MAPPING_NAMES''', '''AutoModel'''),
    ('''audio-classification''', '''MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForAudioClassification'''),
    ('''text-generation''', '''MODEL_FOR_CAUSAL_LM_MAPPING_NAMES''', '''AutoModelForCausalLM'''),
    ('''automatic-speech-recognition''', '''MODEL_FOR_CTC_MAPPING_NAMES''', '''AutoModelForCTC'''),
    ('''image-classification''', '''MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForImageClassification'''),
    ('''image-segmentation''', '''MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES''', '''AutoModelForImageSegmentation'''),
    ('''fill-mask''', '''MODEL_FOR_MASKED_LM_MAPPING_NAMES''', '''AutoModelForMaskedLM'''),
    ('''object-detection''', '''MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES''', '''AutoModelForObjectDetection'''),
    (
        '''zero-shot-object-detection''',
        '''MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES''',
        '''AutoModelForZeroShotObjectDetection''',
    ),
    ('''question-answering''', '''MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES''', '''AutoModelForQuestionAnswering'''),
    ('''text2text-generation''', '''MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES''', '''AutoModelForSeq2SeqLM'''),
    ('''text-classification''', '''MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForSequenceClassification'''),
    ('''automatic-speech-recognition''', '''MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES''', '''AutoModelForSpeechSeq2Seq'''),
    (
        '''table-question-answering''',
        '''MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForTableQuestionAnswering''',
    ),
    ('''token-classification''', '''MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForTokenClassification'''),
    ('''multiple-choice''', '''MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES''', '''AutoModelForMultipleChoice'''),
    (
        '''next-sentence-prediction''',
        '''MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES''',
        '''AutoModelForNextSentencePrediction''',
    ),
    (
        '''audio-frame-classification''',
        '''MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES''',
        '''AutoModelForAudioFrameClassification''',
    ),
    ('''audio-xvector''', '''MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES''', '''AutoModelForAudioXVector'''),
    (
        '''document-question-answering''',
        '''MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForDocumentQuestionAnswering''',
    ),
    (
        '''visual-question-answering''',
        '''MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForVisualQuestionAnswering''',
    ),
    ('''image-to-text''', '''MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES''', '''AutoModelForVision2Seq'''),
    (
        '''zero-shot-image-classification''',
        '''MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES''',
        '''AutoModelForZeroShotImageClassification''',
    ),
    ('''depth-estimation''', '''MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES''', '''AutoModelForDepthEstimation'''),
    ('''video-classification''', '''MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForVideoClassification'''),
    ('''mask-generation''', '''MODEL_FOR_MASK_GENERATION_MAPPING_NAMES''', '''AutoModelForMaskGeneration'''),
]







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'      ,UpperCAmelCase						)
   return [m.group(0						) for m in matches]







def 						_A      ( ):






   '''simple docstring'''







   A__       =     transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
   A__       =     {
       config.replace('Config'      ,''						): model_type for model_type, config in config_maping_names.items()
   }

   # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
   A__       =     collections.defaultdict(UpperCAmelCase						)
   A__       =     collections.defaultdict(UpperCAmelCase						)
   A__       =     collections.defaultdict(UpperCAmelCase						)

   # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
   for attr_name in dir(UpperCAmelCase						):
      A__       =     None
      if _re_tf_models.match(UpperCAmelCase						) is not None:
         A__       =     tf_models
         A__       =     _re_tf_models.match(UpperCAmelCase						).groups()[0]
      elif _re_flax_models.match(UpperCAmelCase						) is not None:
         A__       =     flax_models
         A__       =     _re_flax_models.match(UpperCAmelCase						).groups()[0]
      elif _re_pt_models.match(UpperCAmelCase						) is not None:
         A__       =     pt_models
         A__       =     _re_pt_models.match(UpperCAmelCase						).groups()[0]

      if lookup_dict is not None:
         while len(UpperCAmelCase						) > 0:
            if attr_name in model_prefix_to_model_type:
               A__       =     True
               break
            # Try again after removing the last word in the name
            A__       =     ''.join(camel_case_split(UpperCAmelCase						)[:-1]						)

   A__       =     set(list(pt_models.keys()						) + list(tf_models.keys()						) + list(flax_models.keys()						)						)
   A__       =     list(UpperCAmelCase						)
   all_models.sort()

   A__       =     {'model_type': all_models}
   A__       =     [pt_models[t] for t in all_models]
   A__       =     [tf_models[t] for t in all_models]
   A__       =     [flax_models[t] for t in all_models]

   # Now let's use the auto-mapping names to make sure
   A__       =     {}
   for t in all_models:
      if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
         A__       =     'AutoProcessor'
      elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
         A__       =     'AutoTokenizer'
      elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
         A__       =     'AutoFeatureExtractor'
      else:
         # Default to AutoTokenizer if a model has nothing, for backward compatibility.
         A__       =     'AutoTokenizer'

   A__       =     [processors[t] for t in all_models]

   return pd.DataFrame(UpperCAmelCase						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     [
       transformers_module.models.auto.modeling_auto,
       transformers_module.models.auto.modeling_tf_auto,
       transformers_module.models.auto.modeling_flax_auto,
   ]
   for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
      A__       =     [model_mapping, F"""TF_{model_mapping}""", F"""FLAX_{model_mapping}"""]
      A__       =     [auto_class, F"""TF_{auto_class}""", F"""Flax_{auto_class}"""]
      # Loop through all three frameworks
      for module, cls, mapping in zip(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):
         # The type of pipeline may not exist in this framework
         if not hasattr(UpperCAmelCase      ,UpperCAmelCase						):
            continue
         # First extract all model_names
         A__       =     []
         for name in getattr(UpperCAmelCase      ,UpperCAmelCase						).values():
            if isinstance(UpperCAmelCase      ,UpperCAmelCase						):
               model_names.append(UpperCAmelCase						)
            else:
               model_names.extend(list(UpperCAmelCase						)						)

            # Add pipeline tag and auto model class for those models
         table.update({model_name: (pipeline_tag, cls) for model_name in model_names}						)

   return table







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     get_frameworks_table()
   A__       =     Dataset.from_pandas(UpperCAmelCase						)

   A__       =     hf_hub_download(
       'huggingface/transformers-metadata'      ,'pipeline_tags.json'      ,repo_type='dataset'      ,token=UpperCAmelCase						)
   A__       =     Dataset.from_json(UpperCAmelCase						)
   A__       =     {
       tags_dataset[i]['model_class']: (tags_dataset[i]['pipeline_tag'], tags_dataset[i]['auto_class'])
       for i in range(len(UpperCAmelCase						)						)
   }
   A__       =     update_pipeline_and_auto_class_table(UpperCAmelCase						)

   # Sort the model classes to avoid some nondeterministic updates to create false update commits.
   A__       =     sorted(table.keys()						)
   A__       =     pd.DataFrame(
       {
           'model_class': model_classes,
           'pipeline_tag': [table[m][0] for m in model_classes],
           'auto_class': [table[m][1] for m in model_classes],
       }						)
   A__       =     Dataset.from_pandas(UpperCAmelCase						)

   with tempfile.TemporaryDirectory() as tmp_dir:
      frameworks_dataset.to_json(os.path.join(UpperCAmelCase      ,'frameworks.json'						)						)
      tags_dataset.to_json(os.path.join(UpperCAmelCase      ,'pipeline_tags.json'						)						)

      if commit_sha is not None:
         A__       =     (
             F"""Update with commit {commit_sha}\n\nSee: """
             F"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
         )
      else:
         A__       =     'Update'

      upload_folder(
          repo_id='huggingface/transformers-metadata'      ,folder_path=UpperCAmelCase      ,repo_type='dataset'      ,token=UpperCAmelCase      ,commit_message=UpperCAmelCase      ,)







def 						_A      ( ):






   '''simple docstring'''







   A__       =     {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
   A__       =     transformers_module.pipelines.SUPPORTED_TASKS
   A__       =     []
   for key in pipeline_tasks:
      if key not in in_table:
         A__       =     pipeline_tasks[key]['pt']
         if isinstance(UpperCAmelCase      ,(list, tuple)						):
            A__       =     model[0]
         A__       =     model.__name__
         if model not in in_table.values():
            missing.append(UpperCAmelCase						)

   if len(UpperCAmelCase						) > 0:
      A__       =     ', '.join(UpperCAmelCase						)
      raise ValueError(
          'The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside '
          F"""`utils/update_metadata.py`: {msg}. Please add them!"""						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser()
    parser.add_argument('''--token''', type=str, help='''The token to use to push to the transformers-metadata dataset.''')
    parser.add_argument('''--commit_sha''', type=str, help='''The sha of the commit going with this update.''')
    parser.add_argument('''--check-only''', action='''store_true''', help='''Activate to just check all pipelines are present.''')
    lowerCAmelCase_     						=				parser.parse_args()

    if args.check_only:
        check_pipeline_tags()
    else:
        update_metadata(args.token, args.commit_sha)


